Note on simulation protocol and convention chosen

I created 1 command file for each patche Inside I have one line for each itinerary

With Brieux naming convention : each line get a unique identifier simply simulation_# from 1 to number of itineraries ran.

To add repetitions (see forceeps_replicates analyses) there are multiple possibilities :
- use different seed in the command file and then regroup simulations together. For example simulation 1-10 for the first itinerary, 11-20 for the second… (either by keeping very good track of what is done (and a lot of loops when importing data) or by having a table that gives the correspondances between simulation number and caracteristics of the simulation.)
- change naming convention in forceeps (by modifying brieux scripts to get our own)

# Set working directory for all chunks to parent directory
knitr::opts_knit$set(root.dir = "../../")
# Set global chunk options
knitr::opts_chunk$set(
  echo = FALSE,
  warning = FALSE,
  message = FALSE,
  fig.width = 8,
  fig.height = 8,
  fig.align = "center"
)
## # A tibble: 24 × 4
##    folder           plot_id       simul        simulation_id
##    <chr>            <chr>         <chr>                <int>
##  1 output-cmd_1.txt RETZ_00964_01 simulation_1             1
##  2 output-cmd_1.txt RETZ_00964_01 simulation_2             2
##  3 output-cmd_1.txt RETZ_00964_01 simulation_3             3
##  4 output-cmd_3.txt RETZ_01354_01 simulation_1             4
##  5 output-cmd_3.txt RETZ_01354_01 simulation_2             5
##  6 output-cmd_3.txt RETZ_01354_01 simulation_3             6
##  7 output-cmd_4.txt RETZ_00839_02 simulation_1             7
##  8 output-cmd_4.txt RETZ_00839_02 simulation_2             8
##  9 output-cmd_4.txt RETZ_00839_02 simulation_3             9
## 10 output-cmd_5.txt RETZ_01376_01 simulation_1            10
## # ℹ 14 more rows
## # A tibble: 24 × 4
##    folder           plot_id       simul        simulation_id
##    <chr>            <chr>         <chr>                <int>
##  1 output-cmd_1.txt RETZ_00964_01 simulation_1             1
##  2 output-cmd_1.txt RETZ_00964_01 simulation_2             2
##  3 output-cmd_1.txt RETZ_00964_01 simulation_3             3
##  4 output-cmd_3.txt RETZ_01354_01 simulation_1             4
##  5 output-cmd_3.txt RETZ_01354_01 simulation_2             5
##  6 output-cmd_3.txt RETZ_01354_01 simulation_3             6
##  7 output-cmd_4.txt RETZ_00839_02 simulation_1             7
##  8 output-cmd_4.txt RETZ_00839_02 simulation_2             8
##  9 output-cmd_4.txt RETZ_00839_02 simulation_3             9
## 10 output-cmd_5.txt RETZ_01376_01 simulation_1            10
## # ℹ 14 more rows

Initial Stand Conditions

Initial stand conditions for selected plots
Plot ID Surface (ha) Stand Type Silviculture Species Dominant Species 1 Dominant Species 2 Structure & Soil Cover Basal Area (m²/ha) Mean DBH (cm) Median Age (years)
RETZ_00839_02 2.35 FHETP HET HET F 19.0 22.5 42.0
RETZ_00964_01 11.90 FCHPE CHP CHP F 0.0 7.5 12.0
RETZ_01056_02 1.50 IHETI HET HET ERS I 26.0 45.0 144.5
RETZ_01201_06 0.80 FHET1 HET HET F 22.5 12.5 42.0
RETZ_01252_01 15.47 FHETM HET HET F 27.5 37.5 59.5
RETZ_01354_01 13.61 FHETE HET HET F 0.0 7.5 12.0
RETZ_01376_01 5.91 FHETM HET HET F 24.0 37.5 59.5
RETZ_01642_01 7.03 ICHSI CHS CHS P.S I 22.5 45.0 90.0

The table below summarizes the diversity of the selected plots in terms of stand type and dominant species. This highlights the current selection’s limited heterogeneity, suggesting the need to include a broader range of patches and avoid relying solely on random selection to ensure a more representative sample. (See l.32 in Generate.R)

Also in subsequent analyses I considered each plot as equal but for applicable results :

Itinerary exploration

The study evaluates three distinct silvicultural strategies:

*Note: The decline in basal area observed in simulation 3 (natural evolution) likely results from natural mortality, as the management strategy is set to no intervention (150_3_0.5_0%_FSyl-80).*

Plot-Level Indicators Calculation and Visualization

Over the 80-year simulation period, we calculate the following indicators:

Global Indicators

To create scenarios I combined different itineraries together.

To be able to conclude on different proportions effect and to take into account that the dynamic of one scenario can depend on which plot were chosen I repeated each scenarios 10 time. Exemple : for half clearcut, half no management, I randomly selected half of the plots for each itinerary and repeated this process 10 times to capture variability in the results.

Relationships Between Global Indicators

Future Work